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Model-Based Graphics Recognition

  • Marc Vuilleumier Stückelberg
  • David Doermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1941)

Abstract

In this paper, we illustrate the use of a novel probabilistic framework for document analysis on typical problems of document layout analysis and graphics recognition. Our system uses an explicit descriptive model of the document class to find the most likely interpretation of a scanned document image. In contrast to the traditional pipeline architecture, our system carries out all stages of the analysis with a single inference engine, allowing for an end-to-end propagation of the uncertainty.

Keywords

Document Image Hough Transform Graphic Decomposition Bitmap Image Document Object Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    D. Bainbridge and N. Carter. Automatic Reading of Music Notation, in H. Bunke and P. S. P. Wang editors, Handbook of Character Recognition and Document Image Analysis, World Scientific, 1997.Google Scholar
  2. [2]
    D. Blostein and H. Baird. A Critical Survey of Music Image Analysis, in H. Baird, H. Bunke and K. Yamamoto editors, Structured Document Image Analysis, Springer Verlag, 1992.Google Scholar
  3. [3]
    N. Carter, R. Bacon and T. Messenger. The acquisition, Representation and Reconstruction of Printed Music by Computer: A Review. Computers and the Humanities, 22:117–136, 1988.CrossRefGoogle Scholar
  4. [4]
    D. Bainbridge. Extensible Optical Music Recognition. Ph.D. Thesis, University of Canterbury, 1997.Google Scholar
  5. [5]
    B. Coüasnon and J. Camillerapp. Using Grammars to Segment and Recognize Music Scores. IAPR Workshop on Document Analysis Systems, Kaiserslautern, Germany, 1994.Google Scholar
  6. [6]
    H. Fahmy and D. Blostein. A Graph Grammar for High-Level Recognition of Music Notation. Proceedings of ICDAR’91, Saint Malo, France, 1991.Google Scholar
  7. [7]
    H. Kato and S. Inokuchi. A Recognition System for Printer Piano Music Using Musical Knowldge and Constraints, in H. Baird, H. Bunke and K. Yamamoto editors, Structured Document Image Analysis, Springer-Verlag, 1992.Google Scholar
  8. [8]
    G. Kopec and P. Chou. Document Image Decoding Using Markov Source Models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(6):602–617, June 1994.CrossRefGoogle Scholar
  9. [9]
    G. Kopec, P. Chou and D. Maltz. Markov Source Model for Printed Music Decoding. Journal of Electronic Imaging, 5(1):7–14, January 1996.CrossRefGoogle Scholar
  10. [10]
    M. Vuilleumier Stückelberg and D. Doermann. On Musical Score Recognition using Probabilistic Reasoning. Proceedings of ICDAR’99, Bangalore, India, September 1999.Google Scholar
  11. [11]
    J. Maxwell III and R. Kaplan. The Interface between Phrasal and Functional Constraints. Computational Linguistics, 19(4):571–589, 1994.Google Scholar
  12. [12]
    I. Fujinaga. Optical Music Recognition using Projections. M.S. Thesis, McGill University, Montreal, Canada, 1988.Google Scholar
  13. [13]
    R. Prokop and A. Reeves. A Survey of Moment-Based Techniques for Unoccluded Object Representation and Recognition. Computer Vision, Graphics and Image Processing, 54(5):438–460, September 1992.Google Scholar
  14. [14]
    L. Rabiner and B.-H. Juang. Fundamentals of Speech Recognition. Prentice-Hall, 1993.Google Scholar
  15. [15]
    P. Cheeseman et al. AutoClass: A Bayesian Classification System. Fifth Int. Workshop on Machine Learning, Ann Arbor, 1988.Google Scholar
  16. [16]
    C. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.Google Scholar
  17. [17]
    J. Illingworth and J. Kittler. A Survey of the Hough Transform. Computer Vision, Graphics and Image Processing, 44(1):87–116, January 1988.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Marc Vuilleumier Stückelberg
    • 1
  • David Doermann
    • 2
  1. 1.CUIUniversity of GenevaGeneva 4Switzerland
  2. 2.LAMPUniversity of MarylandUSA

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